R6 Class Representing PosteriorBSVART
The class PosteriorBSVART contains posterior output and the specification including the last MCMC draw for the bsvar model with t-distributed structural shocks. Note that due to the thinning of the MCMC output the starting value in element last_draw
might not be equal to the last draw provided in element posterior.
# This is a function that is used within estimate() data(us_fiscal_lsuw) specification = specify_bsvar_t$new(us_fiscal_lsuw, p = 4) set.seed(123) estimate = estimate(specification, 10) class(estimate) ## ------------------------------------------------ ## Method `specify_posterior_bsvar_t$get_posterior` ## ------------------------------------------------ data(us_fiscal_lsuw) specification = specify_bsvar_t$new(us_fiscal_lsuw) set.seed(123) estimate = estimate(specification, 10) estimate$get_posterior() ## ------------------------------------------------ ## Method `specify_posterior_bsvar_t$get_last_draw` ## ------------------------------------------------ data(us_fiscal_lsuw) # specify the model and set seed specification = specify_bsvar_t$new(us_fiscal_lsuw, p = 4) # run the burn-in set.seed(123) burn_in = estimate(specification, 10) # estimate the model posterior = estimate(burn_in, 10) ## ------------------------------------------------ ## Method `specify_posterior_bsvar_t$is_normalised` ## ------------------------------------------------ # upload data data(us_fiscal_lsuw) # specify the model and set seed specification = specify_bsvar_t$new(us_fiscal_lsuw, p = 4) # estimate the model set.seed(123) posterior = estimate(specification, 10) # check normalisation status beforehand posterior$is_normalised() # normalise the posterior BB = posterior$last_draw$starting_values$B # get the last draw of B B_hat = diag((-1) * sign(diag(BB))) %*% BB # set negative diagonal elements normalise_posterior(posterior, B_hat) # draws in posterior are normalised # check normalisation status afterwards posterior$is_normalised() ## ------------------------------------------------ ## Method `specify_posterior_bsvar_t$set_normalised` ## ------------------------------------------------ # This is an internal function that is run while executing normalise_posterior() # Observe its working by analysing the workflow: # upload data data(us_fiscal_lsuw) # specify the model and set seed specification = specify_bsvar_t$new(us_fiscal_lsuw, p = 4) set.seed(123) # estimate the model posterior = estimate(specification, 10) # check normalisation status beforehand posterior$is_normalised() # normalise the posterior BB = posterior$last_draw$starting_values$B # get the last draw of B B_hat = diag(sign(diag(BB))) %*% BB # set positive diagonal elements normalise_posterior(posterior, B_hat) # draws in posterior are normalised # check normalisation status afterwards posterior$is_normalised()
estimate, specify_bsvar_t
last_draw: an object of class BSVART with the last draw of the current MCMC run as the starting value to be passed to the continuation of the MCMC estimation using estimate().
posterior: a list containing Bayesian estimation output.
new()Create a new posterior output PosteriorBSVART.
specify_posterior_bsvar_t$new(specification_bsvar, posterior_bsvar)
specification_bsvar: an object of class BSVART with the last draw of the current MCMC run as the starting value.
posterior_bsvar: a list containing Bayesian estimation output.
A posterior output PosteriorBSVART.
get_posterior()Returns a list containing Bayesian estimation output.
specify_posterior_bsvar_t$get_posterior()
data(us_fiscal_lsuw)
specification = specify_bsvar_t$new(us_fiscal_lsuw)
set.seed(123)
estimate = estimate(specification, 10)
estimate$get_posterior()
get_last_draw()Returns an object of class BSVART with the last draw of the current MCMC run as the starting value to be passed to the continuation of the MCMC estimation using estimate().
specify_posterior_bsvar_t$get_last_draw()
data(us_fiscal_lsuw)
# specify the model and set seed
specification = specify_bsvar_t$new(us_fiscal_lsuw, p = 4)
# run the burn-in
set.seed(123)
burn_in = estimate(specification, 10)
# estimate the model
posterior = estimate(burn_in, 10)
is_normalised()Returns TRUE if the posterior has been normalised using normalise_posterior() and FALSE otherwise.
specify_posterior_bsvar_t$is_normalised()
# upload data
data(us_fiscal_lsuw)
# specify the model and set seed
specification = specify_bsvar_t$new(us_fiscal_lsuw, p = 4)
# estimate the model
set.seed(123)
posterior = estimate(specification, 10)
# check normalisation status beforehand
posterior$is_normalised()
# normalise the posterior
BB = posterior$last_draw$starting_values$B # get the last draw of B
B_hat = diag((-1) * sign(diag(BB))) %*% BB # set negative diagonal elements
normalise_posterior(posterior, B_hat) # draws in posterior are normalised
# check normalisation status afterwards
posterior$is_normalised()
set_normalised()Sets the private indicator normalised to TRUE.
specify_posterior_bsvar_t$set_normalised(value)
value: (optional) a logical value to be passed to indicator normalised.
# This is an internal function that is run while executing normalise_posterior()
# Observe its working by analysing the workflow:
# upload data
data(us_fiscal_lsuw)
# specify the model and set seed
specification = specify_bsvar_t$new(us_fiscal_lsuw, p = 4)
set.seed(123)
# estimate the model
posterior = estimate(specification, 10)
# check normalisation status beforehand
posterior$is_normalised()
# normalise the posterior
BB = posterior$last_draw$starting_values$B # get the last draw of B
B_hat = diag(sign(diag(BB))) %*% BB # set positive diagonal elements
normalise_posterior(posterior, B_hat) # draws in posterior are normalised
# check normalisation status afterwards
posterior$is_normalised()
clone()The objects of this class are cloneable with this method.
specify_posterior_bsvar_t$clone(deep = FALSE)
deep: Whether to make a deep clone.